While many studies point to racial/ethnic, economic, and education-related disparities in health and healthcare access, little is known about people with disabilities, a population recently recognized by the CDC as an at-risk demographic group. In this extensively revised application, we propose to show empirically the extent to which each access, coordination, and quality of care influence the probability, timing and number of avoidable hospitalizations, that is, hospitalizations secondary to Ambulatory Care Sensitive (ACS) conditions, and their related costs. Specifically, our aims are to: (1) determine the extent to which stages of limitation influence (i) access barriers to medical care, (ii) care coordination& quality, (iii) actual receipt of guideline compliant care, (iv) rates of ACS hospitalizations and () ACS-related hospitalization costs; (2) examine the relationship between access barriers, care coordination & quality, and actual receipt of guideline compliant care, and evaluate whether these relationships vary according to stages of limitation; (3) determine the extent to which (i) access barriers to care, (ii) care coordination & quality, and (iii) actual receipt of guideline compliant care are associated with ACS hospitalizations and related costs and (4) perform a policy simulation to estimate the relative contribution of programs aimed at (i) reducing access barriers, (ii) improving coordination & quality of care and (iii) enhancing provision of guideline compliant care in reducing ACS-related hospitalizations and associated costs. These aims will be carried out by analyzing longitudinal data from the Medicare Current Beneficiary Surveys (MCBS) aggregated over a 10-year period. Following direct standardization methods, we will apply two-stage and competing risk duration models to estimate the independent effects of measures capturing activity limitation stage, access barriers, coordination & perceived quality of care, and degree of guideline compliant care on ACS hospitalizations and costs. In addition to filling an essential knowledge gap by providing mechanistic information about potential sources of disparities in the care of persons with disabilities, this study will also provide an innovative policy simulation that will inform efforts to determine how best to allocate public resources in order to remediate disability disparities in avoidable hospitalizations and realize the greatest cost savings.